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DEFO-NET: Learning body deformation using generative adversarial networks

Abstract:

Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2018.8462832

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
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Publisher:
IEEE Publisher's website
Journal:
IEEE International Conference on Robotics and Automation (ICRA 2018) Journal website
Pages:
2440-2447
Host title:
2018 IEEE International Conference on Robotics and Automation (ICRA)
Publication date:
2018-09-13
Acceptance date:
2018-01-08
DOI:
ISSN:
2577-087X
Source identifiers:
827797
ISBN:
9781538630815
Pubs id:
pubs:827797
UUID:
uuid:36b98aa4-1711-47f9-91de-0e41e9c5baeb
Local pid:
pubs:827797
Deposit date:
2018-03-05

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